1. The Field of the Invention
The present invention relates to medical imaging. More specifically, embodiments of the present invention relates to systems and methods for using an image reconstruction algorithm to perform medical imaging.
2. The Relevant Technology
Electroencephalography (EEG) and magnetoencephalography (MEG) are examples of medical imaging techniques currently used in clinical and research settings. Specifically, EEG and MEG are used to measure the electrical and magnetic fields generated directly by the electrical activity of the brain. EEGs and MEGs are typically used in functional brain imaging by performing a non-invasive procedure wherein the patient's neural activity is detected using an array of sensors placed on the outside of the patient's head. Once the patient's neural activity is captured, signal processing techniques are used to determine the location of the patient's underlying neural sources.
The use of MEG/EEG measurements to obtain precise spatial localization of brain activity regions has been a topic of intense research for the last two decades with numerous different signal processing techniques being developed. Most procedures addressing the inverse EEG and MEG problems can be categorized as either multiple dipole or distributed source (or imaging) methods.
The multiple dipole approaches assume the regional brain activation to be relatively focal, so that it can be well accounted for by a small number of current dipoles. This category of techniques rely on dipole fitting algorithms, such as the ones available in the Brain Electrical Source Analysis “BESA” software distributed by MEGIS Software GmbH. The multiple dipole approaches typically use non-linear optimization procedures, such as gradient steepest descent or downhill simplex algorithms, to search for the dipole parameters (including location, orientation and strength) that best explain the measured data in a deterministic least squares sense. Unfortunately, however, there are well-documented drawbacks of the multiple dipole methods, such as their high sensitivity to user-established initial conditions, including the number of dipoles and their initial parameters. Because of the high non-convexity of the error surface with respect to the dipole locations, the optimization algorithms may erroneously converge to local minima, particularly when there are a large number of unknown parameters. Another algorithm referred to as the multiple signal classification or “MUSIC” algorithm, makes use of signal subspace techniques in order to overcome some of the drawbacks of the multiple dipole approaches, but still may perform poorly when sources are distributed or highly correlated.
Alternatively, distributed source models have also been used to estimate the sources of brain activity. These models have also been intensely scrutinized, particularly because they overcome some of the limitations of the multiple dipole models, including those mentioned above. In the distributed source model approaches, the brain or restricted cortical mantle is considered as a source space comprised of a lattice of points, which include the locations of a large number of dipoles. The main advantage of the distributed source models is that the sources parameters may be estimated from the measured data without requiring a priori information about the number or location of the active sources. Thus, the distributed source models are less sensitive to a set of initial conditions as the multiple dipole approaches. Unfortunately, however, because the estimation is an ill-posed image reconstruction problem, even the distributed source models still require a priori constraints, although the a priori constraints for the distributed source models are different than the constraints of the multiple dipole strategy.
In various distributed source model implementations currently known in the art, such as the CURRY and SOURCE 5 source reconstruction software distributed by Compumedics Neuroscan, a minimum-norm estimate or “MNE” approach seeks a distribution of dipoles that matches the measured data and has a minimum power. While this approach is suitable for smooth, extended active brain regions, its estimates can be systematically biased towards the outer brain surface and are always smeared due to the approach's tendency to overestimate the spatial extension of the source pattern. Hence, the approach often provides source estimates with a spatial distribution that is much more extended than the expected focal brain activity in real brain measurements.
One possible solution to overcome the bias and smearing problems of the MNE approach has been proposed, wherein an iteratively re-weighted minimum norm method, such as the FOCal Underdetermined System Solution or “FOCUSS” approach is used. In the FOCUSS method, iterative approach based on a regularized Least Squares formulation is used with an initial solution obtained via a Moore-Penrose pseudoinverse. More specifically, the initial pseudoinverse solution is then employed to weight the Least Squares estimate of the updated solution. One drawback of this approach, however, is that there is a strong bias towards solutions that identify activity close to the outer surface of the brain, due to the inherited bias of the initialization step. The reason for such bias is that the attenuation of a given MEG signal is a function of the physical depth of the generator location or the relative distance to the sensors. Although some adjustments have been proposed to remove the biasing of the FOCUSS algorithm, including attempts to adjust the initialization and/or iterative steps of the algorithm, the adjustments appear ad hoc and the successful removal of biasing has not yet been achieved.
A final class of imaging techniques recently proposed for MEG purposes use beamforming principles and are sometimes called covariance-based techniques since they require good estimates of the spatio-temporal data covariance matrix. Examples of this approach include the linearly-constrained minimum variance beamformer or “LCMV” approach and the Synthetic Aperture Magmetometry or “SAM” approach. Neither the LCMV or SAM solution requires prior knowledge of the source location and activity, including the dipole orientations and/or magnitudes, for the reconstruction process. One problem with both methods, however, is that both LCMV and SAM assume there is no temporal correlation between the activities of neural sources at different locations in the brain. When such temporal correlation is present, this type of beamforming may result in erroneous signal cancellation.
Recently, modifications to the LCMV technique have been proposed and evaluated for auditory steady state responses, although the proposed technique requires prior knowledge of interferer locations which are generally unknown in experimental data. Another drawback of this type of beamforming is the relatively large number of time samples required to effectively construct a good estimator of the data covariance matrix. Additionally, the spatial resolution is fundamentally limited by the number of sensors in the array.
The subject matter claimed herein is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one exemplary technology area where some embodiments described herein may be practiced.